{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/ibm_watsonx.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IBM watsonx.ai\n",
    "\n",
    ">WatsonxEmbeddings is a wrapper for IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) embedding models.\n",
    "\n",
    "This example shows how to communicate with `watsonx.ai` embedding models using the `LlamaIndex` Embeddings API."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up\n",
    "\n",
    "Install the `llama-index-embeddings-ibm` package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -qU llama-index-embeddings-ibm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below defines the credentials required to work with watsonx Embeddings.\n",
    "\n",
    "**Action:** Provide the IBM Cloud user API key. For details, see\n",
    "[Managing user API keys](https://cloud.ibm.com/docs/account?topic=account-userapikey&interface=ui)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from getpass import getpass\n",
    "\n",
    "watsonx_api_key = getpass()\n",
    "os.environ[\"WATSONX_APIKEY\"] = watsonx_api_key"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, you can pass additional secrets as an environment variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"WATSONX_URL\"] = \"your service instance url\"\n",
    "os.environ[\"WATSONX_TOKEN\"] = \"your token for accessing the CPD cluster\"\n",
    "os.environ[\"WATSONX_PASSWORD\"] = \"your password for accessing the CPD cluster\"\n",
    "os.environ[\"WATSONX_USERNAME\"] = \"your username for accessing the CPD cluster\"\n",
    "os.environ[\n",
    "    \"WATSONX_INSTANCE_ID\"\n",
    "] = \"your instance_id for accessing the CPD cluster\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the model\n",
    "\n",
    "You might need to adjust embedding parameters for different tasks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "truncate_input_tokens = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the `WatsonxEmbeddings` class with the previously set parameter.\n",
    "\n",
    "\n",
    "**Note**: \n",
    "\n",
    "- To provide context for the API call, you must pass the `project_id` or `space_id`. To get your project or space ID, open your project or space, go to the **Manage** tab, and click **General**. For more information see: [Project documentation](https://www.ibm.com/docs/en/watsonx-as-a-service?topic=projects) or [Deployment space documentation](https://www.ibm.com/docs/en/watsonx/saas?topic=spaces-creating-deployment).\n",
    "- Depending on the region of your provisioned service instance, use one of the urls listed in [watsonx.ai API Authentication](https://ibm.github.io/watsonx-ai-python-sdk/setup_cloud.html#authentication).\n",
    "\n",
    "In this example, we’ll use the `project_id` and Dallas URL.\n",
    "\n",
    "\n",
    "You need to specify the `model_id` that will be used for inferencing. You can find the list of all the available models in [Supported foundation models](https://ibm.github.io/watsonx-ai-python-sdk/fm_model.html#ibm_watsonx_ai.foundation_models.utils.enums.ModelTypes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.embeddings.ibm import WatsonxEmbeddings\n",
    "\n",
    "watsonx_embedding = WatsonxEmbeddings(\n",
    "    model_id=\"ibm/slate-125m-english-rtrvr\",\n",
    "    url=\"https://us-south.ml.cloud.ibm.com\",\n",
    "    project_id=\"PASTE YOUR PROJECT_ID HERE\",\n",
    "    truncate_input_tokens=truncate_input_tokens,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can use Cloud Pak for Data credentials. For details, see [watsonx.ai software setup](https://ibm.github.io/watsonx-ai-python-sdk/setup_cpd.html).    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "watsonx_embedding = WatsonxEmbeddings(\n",
    "    model_id=\"ibm/slate-125m-english-rtrvr\",\n",
    "    url=\"PASTE YOUR URL HERE\",\n",
    "    username=\"PASTE YOUR USERNAME HERE\",\n",
    "    password=\"PASTE YOUR PASSWORD HERE\",\n",
    "    instance_id=\"openshift\",\n",
    "    version=\"4.8\",\n",
    "    project_id=\"PASTE YOUR PROJECT_ID HERE\",\n",
    "    truncate_input_tokens=truncate_input_tokens,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Usage\n",
    "\n",
    "### Embed query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.05538924, 0.05161056, 0.01207759, 0.0017501727, -0.017691258]\n"
     ]
    }
   ],
   "source": [
    "query = \"Example query.\"\n",
    "\n",
    "query_result = watsonx_embedding.get_query_embedding(query)\n",
    "print(query_result[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Embed list of texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.009447167, -0.024981938, -0.02601326, -0.04048393, -0.05780444]\n"
     ]
    }
   ],
   "source": [
    "texts = [\"This is a content of one document\", \"This is another document\"]\n",
    "\n",
    "doc_result = watsonx_embedding.get_text_embedding_batch(texts)\n",
    "print(doc_result[0][:5])"
   ]
  }
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